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• W2103180882 abstract "Copper is essential for human physiology, but in excess it causes the severe metabolic disorder Wilson disease. Elevated copper is thought to induce pathological changes in tissues by stimulating the production of reactive oxygen species that damage multiple cell targets. To better understand the molecular basis of this disease, we performed genome-wide mRNA profiling as well as protein and metabolite analysis for Atp7b-/- mice, an animal model of Wilson disease. We found that at the presymptomatic stages of the disease, copper-induced changes are inconsistent with widespread radical-mediated damage, which is likely due to the sequestration of cytosolic copper by metallothioneins that are markedly up-regulated in Atp7b-/- livers. Instead, copper selectively up-regulates molecular machinery associated with the cell cycle and chromatin structure and down-regulates lipid metabolism, particularly cholesterol biosynthesis. Specific changes in the transcriptome are accompanied by distinct metabolic changes. Biochemical and mass spectroscopy measurements revealed a 3.6-fold decrease of very low density lipoprotein cholesterol in serum and a 33% decrease of liver cholesterol, indicative of a marked decrease in cholesterol biosynthesis. Consistent with low cholesterol levels, the amount of activated sterol regulatory-binding protein 2 (SREBP-2) is increased in Atp7b-/- nuclei. However, the SREBP-2 target genes are dysregulated suggesting that elevated copper alters SREBP-2 function rather than its processing or re-localization. Thus, in Atp7b-/- mice elevated copper affects specific cellular targets at the transcription and/or translation levels and has distinct effects on liver metabolic function, prior to appearance of histopathological changes. The identification of the network of specific copper-responsive targets facilitates further mechanistic analysis of human disorders of copper misbalance. Copper is essential for human physiology, but in excess it causes the severe metabolic disorder Wilson disease. Elevated copper is thought to induce pathological changes in tissues by stimulating the production of reactive oxygen species that damage multiple cell targets. To better understand the molecular basis of this disease, we performed genome-wide mRNA profiling as well as protein and metabolite analysis for Atp7b-/- mice, an animal model of Wilson disease. We found that at the presymptomatic stages of the disease, copper-induced changes are inconsistent with widespread radical-mediated damage, which is likely due to the sequestration of cytosolic copper by metallothioneins that are markedly up-regulated in Atp7b-/- livers. Instead, copper selectively up-regulates molecular machinery associated with the cell cycle and chromatin structure and down-regulates lipid metabolism, particularly cholesterol biosynthesis. Specific changes in the transcriptome are accompanied by distinct metabolic changes. Biochemical and mass spectroscopy measurements revealed a 3.6-fold decrease of very low density lipoprotein cholesterol in serum and a 33% decrease of liver cholesterol, indicative of a marked decrease in cholesterol biosynthesis. Consistent with low cholesterol levels, the amount of activated sterol regulatory-binding protein 2 (SREBP-2) is increased in Atp7b-/- nuclei. However, the SREBP-2 target genes are dysregulated suggesting that elevated copper alters SREBP-2 function rather than its processing or re-localization. Thus, in Atp7b-/- mice elevated copper affects specific cellular targets at the transcription and/or translation levels and has distinct effects on liver metabolic function, prior to appearance of histopathological changes. The identification of the network of specific copper-responsive targets facilitates further mechanistic analysis of human disorders of copper misbalance. Copper plays an essential role in human physiology. It serves as a cofactor of key metabolic enzymes and is required for embryonic development, neuronal myelination, radical detoxification, and numerous other physiological processes. Mutations in copper-binding proteins have been linked to such devastating disorders as amyotrophic lateral sclerosis, Alzheimer disease, prion disease, and Menkes disease. In Wilson disease (WD), 4The abbreviations used are: WD, Wilson disease; VLDL, very low density lipoprotein; LDL, low density lipoprotein; LDLR, low density lipoprotein receptor; WT, wild type; KO, knock-out; DTT, dithiothreitol; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonic acid; RT, real time; HMG, hydroxymethylglutaryl; MS/MS, tandem mass spectrometry; CAPS, 3-(cyclohexylamino)propanesulfonic acid; GO, Gene Ontology; SREBP-2, sterol regulatory-binding protein 2; SOD1, superoxide dismutase 1. 4The abbreviations used are: WD, Wilson disease; VLDL, very low density lipoprotein; LDL, low density lipoprotein; LDLR, low density lipoprotein receptor; WT, wild type; KO, knock-out; DTT, dithiothreitol; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonic acid; RT, real time; HMG, hydroxymethylglutaryl; MS/MS, tandem mass spectrometry; CAPS, 3-(cyclohexylamino)propanesulfonic acid; GO, Gene Ontology; SREBP-2, sterol regulatory-binding protein 2; SOD1, superoxide dismutase 1. the direct link between elevated hepatic copper and development of liver pathology has been firmly established. The disease is caused by mutations of the copper-transporting ATPase ATP7B (Wilson disease protein) (1Cox D. Roberts E. Feldman M. Friedman L. Brandt L.S. Wilson Disease, W. B. Saunders Co., Philadelphia. 2006: 1601-1612Google Scholar, 2Ferenci P. Metab. Brain Dis. 2004; 19: 229-239Crossref PubMed Scopus (107) Google Scholar, 3Gitlin J.D. Gastroenterology. 2003; 125: 1868-1877Abstract Full Text Full Text PDF PubMed Scopus (309) Google Scholar). ATP7B is expressed predominantly in the liver, where it transports copper from the cytosol into the lumen of the Golgi network for incorporation into ceruloplasmin, a copper-dependent ferroxidase. ATP7B is also required to export excess copper from the liver into the bile; this represents the major excretory route for copper in the body (4Loudianos G. Gitlin J.D. Semin. Liver Dis. 2000; 20: 353-364Crossref PubMed Scopus (141) Google Scholar). In WD patients, both functions are disrupted, and copper accumulates to levels that are 10-20-fold higher than the norm (5Brewer G.J. Am. J. Clin. Nutr. 1998; 67: S1087-S1090Crossref Scopus (36) Google Scholar). Gradual copper accumulation, most noticeable in the liver, induces marked changes in tissue structure and function. Liver injury is the most common manifestation of WD, although neurological and psychiatric symptoms are also frequently observed (2Ferenci P. Metab. Brain Dis. 2004; 19: 229-239Crossref PubMed Scopus (107) Google Scholar, 4Loudianos G. Gitlin J.D. Semin. Liver Dis. 2000; 20: 353-364Crossref PubMed Scopus (141) Google Scholar, 6Stremmel W. Meyerrose K.W. Niederau C. Hefter H. Kreuzpaintner G. Strohmeyer G. Ann. Intern. Med. 1991; 115: 720-726Crossref PubMed Scopus (261) Google Scholar). WD patients may show progressive hepatic cirrhosis, chronic active hepatitis, or rapidly developing liver failure (7Riordan S.M. Williams R. J. Hepatol. 2001; 34: 165-171Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar). None of these clinical features is specific to WD, complicating its diagnosis. Remarkably, despite a long history (WD was described in 1912 (8Wilson S.A.K. Brain. 1912; 34: 295-507Crossref Scopus (766) Google Scholar)) and significant progress in characterization of its genetic basis (9Tanzi R.E. Petrukhin K. Chernov I. Pellequer J.L. Wasco W. Ross B. Romano D.M. Parano E. Pavone L. Brzustowicz L.M. Devoto M. Peppercorn J. Bush A.T. Sternlieb I. Pirastu M. Gusella J.F. Evgrafov O. Penchaszadeh G.K. Honig B. Edelman I.S. Soares M.B. Scheinberg I.H. Gilliam T.C. Nat. Genet. 1993; 5: 344-350Crossref PubMed Scopus (1177) Google Scholar, 10Bull P.C. Thomas G.R. Rommens J.M. Forbes J.R. Cox D.W. Nat. Genet. 1993; 5: 327-337Crossref PubMed Scopus (1695) Google Scholar, 11Yamaguchi Y. Heiny M.E. Gitlin J.D. Biochem. Biophys. Res. Commun. 1993; 197: 271-277Crossref PubMed Scopus (473) Google Scholar), the molecular and metabolic changes that accompany initial stages of copper accumulation (and may serve as markers of disease progression) remain poorly characterized. The effects of accumulated copper on lipid peroxidation, enzyme activity, and DNA stability have been reported in both patients and in animal models of WD (12Britton R.S. Semin. Liver Dis. 1996; 16: 3-12Crossref PubMed Scopus (229) Google Scholar, 13Gaetke L.M. Chow C.K. Toxicology. 2003; 189: 147-163Crossref PubMed Scopus (1457) Google Scholar, 14Linder M.C. Mutat. Res. 2001; 475: 141-152Crossref PubMed Scopus (149) Google Scholar), yet it is still unclear which of these manifestations are the primary effects of accumulated copper and which are the longer term consequences of the disease. Understanding the initial copper-specific changes is particularly important, not only because detecting such changes may serve as a useful diagnostic tool, but also because corrective or supportive treatment can be implemented to overcome observed metabolic alterations and further improve and supplement copper-chelation therapy. To understand molecular events associated with the early stages of copper overload, we have utilized Atp7b-/- mice, an animal model of WD. We have shown previously that these animals accumulate copper to high levels and have several phenotypic features resembling WD (15Huster D. Finegold M.J. Morgan C.T. Burkhead J.L. Nixon R. Vanderwerf S.M. Gilliam C.T. Lutsenko S. Am. J. Pathol. 2006; 168: 423-434Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar). We have also observed that in the 6-week-old Atp7b-/- mice, the hepatic copper is at its highest level, yet the pathology is still minimal (15Huster D. Finegold M.J. Morgan C.T. Burkhead J.L. Nixon R. Vanderwerf S.M. Gilliam C.T. Lutsenko S. Am. J. Pathol. 2006; 168: 423-434Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar). At this presymptomatic stage the changes in gene/protein expression as well as associated metabolic alterations in the liver are likely to represent specific responses to copper. In this study, we characterize changes in the hepatic mRNAs, proteins, and metabolites in response to copper accumulation in 6-week-old Atp7b-/- mice. We demonstrate that the effect of copper is selective and involves distinct metabolic pathways, organized in the interconnected network, although oxidative stress is not apparent. The most significant and unexpected are the up-regulation of cell cycle machinery and the down-regulation of cholesterol metabolism; the latter is accompanied by marked decrease of cholesterol in the liver. These novel findings pave the way for detailed mechanistic analysis of WD and other copper-induced pathologies. Animals—The generation of the Atp7b-/- mouse has been described previously (16Buiakova O.I. Xu J. Lutsenko S. Zeitlin S. Das K. Das S. Ross B.M. Mekios C. Scheinberg I.H. Gilliam T.C. Hum. Mol. Genet. 1999; 8: 1665-1671Crossref PubMed Scopus (164) Google Scholar). Mice were maintained on strain C57BL × 129S6/SvEv, and female animals at 6 weeks of age were used for microarray and RT-PCR studies. The Atp7b-/- and control mice were housed at the Oregon Health & Science University animal facility according to the National Institutes of Health guidelines on the use of laboratory and experimental animals. Food and water were provided ad libitum, and no further treatment was carried out. Animals were euthanized at given time points and livers quickly removed for different tissue preparations. RNA Isolation for Microarray—Immediately after removal, the liver pieces (∼75 mg) were frozen in liquid nitrogen and stored until further use. The total RNA was isolated using TRIzol reagent (Invitrogen) according to the manufacturer’s protocol followed by an RNeasy cleanup procedure (Qiagen). The integrity of isolated RNA was electrophoretically verified by ethidium bromide staining and by absorbance ratio (A260 nm/280 nm > 1.8). A total of six liver RNA samples (three biological replicates for WT and three for KO animals) were tested for RNA quality. Each of the six sample targets was hybridized to two different MOE430A Affymetrix GeneChip arrays (technical replicates). The Affymetrix chip contained 22,691 spots corresponding to 13,016 unique Unigene IDs (i.e. unique genes). Chip Performance Analysis—Image processing and analysis were performed using Affymetrix MAS 5.0 software. The resulting intensities and coordinate information were saved in a CEL file format and then subjected to global scaling with an average target intensity of 350 to allow for direct comparison of hybridization values from different targets. Scaled results for each sample were saved as CHP files, and these data were used to evaluate overall chip performance (supplemental Table 1). The analysis indicated that the parameters describing the quality of RNA, hybridization, and detection were all within acceptable range. The data in CEL format were imported into GeneSifter.net, a microarray data analysis application and subjected to RMA normalization using published algorithms (17Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8415) Google Scholar). The RMA-normalized data set was then used to identify changed genes and determine the statistical significance and magnitude of changes (fold change). The significance was established using Wilcoxon t test with Benjamini and Hochberg adjustment for multiple comparisons. The data were annotated with NCBI Unigene IDs and with related Gene Ontology terms by using the Affymetrics NetAffx tool and NIAID-NIH DAVID (apps1.niaid.nih.gov/david/). Of the 310 significantly differentially expressed probe sets, 304 had Unigene IDs and were annotated in mouse Gene Ontology (GO). In a number of cases, when changes in the transcript were detected by more than one probe set, the sequences of these probe sets were manually compared with the sequence of the identified transcript to ensure that the probe sets indeed mapped to the same gene. The GO analysis was carried out using GenMapp on-line tool (18Dahlquist K.D. Salomonis N. Vranizan K. Lawlor S.C. Conklin B.R. Nat. Genet. 2002; 31: 19-20Crossref PubMed Scopus (804) Google Scholar, 19Doniger S.W. Salomonis N. Dahlquist K.D. Vranizan K. Lawlor S.C. Conklin B.R. Genome Biol. 2003; 4: R7Crossref PubMed Google Scholar). For each identified GO term, Gene Map z-statistic was calculated. A positive z-statistic for any particular GO term indicated that there were more genes associated with this GO term than would be expected by random chance. The GO terms with positive z-statistic were then ranked based on the percent of changed genes that belong to the corresponding GO compared with the total number of genes on a chip with the same GO. This analysis identified those GO terms that were most strongly represented among the differentially expressed genes (probe sets). The results were independently confirmed by applying the GO Browser tool in NetAffx to the same set of differentially expressed genes. The gene expression data have been submitted to GEO data base (GEO number GSE5348). The associations between altered genes or pathways were further evaluated using the Ingenuity Pathways Analysis software (Ingenuity® Systems). Affymetrix identifiers of the differentially expressed genes (the fold change of 1.5 or higher) and their corresponding expression values were loaded into the software and mapped to its corresponding gene object (so-called focus genes) in the Ingenuity Pathways Knowledge Base. The significance of the associations between the data set and the canonical pathway and functional annotations was calculated in two ways. First, the number of genes from the data set that map to the pathway was divided by the number of all known genes ascribed to the pathway. Second, the left-tailed Fisher exact test was used to calculate related p values and distinguish those functional/pathway annotations that had more focus genes than expected by chance. The networks of the focus genes were algorithmically generated based on their connectivity. Real Time PCR (RT-PCR)—Total RNA was extracted from livers of 4-, 6-, and 32-week-old WT and KO mice (n = 6 per group) and checked for integrity as described above. One-step RT-PCR was performed with a LightCycler instrument (Roche Applied Science) in a total volume of 20 μl containing 50 ng of total RNA, 0.5 μm each primer, LightCycler RT-PCR reaction mix SYBR Green I (1×), and LightCycler RT-PCR Enzyme Mix (Roche Applied Science). Reverse transcription was performed at 50 °C for 20 min. The denaturation and amplification conditions were 95 °C for 15 s followed by up to 35 cycles of PCR. Each cycle of PCR included denaturation at 95 °C for 15 s and then 10 s of primer annealing at 55 °C and 20 s of extension/synthesis at 72 °C (20 s). The temperature ramp was 20 °C/s, except when heating to 72 °C, when it was 2 °C/s. At the end of the extension, step fluorescence of each sample was measured to allow quantification of the RNA. After amplification a melting curve was obtained by heating at 20 °C/s to 95 °C, cooling at 20 °C/s to 60 °C, and slowly heating at 0.1 °C/s to 90 °C with fluorescence data collection at 0.1 °C intervals. RT-PCR primers for the internal standard glyceraldehyde-3-phosphate dehydrogenase gene and the target genes were designed using Omiga™ software (Oxford Molecular, Oxford, UK). Gene sequences were taken from the Affymetrix web site based on Affymetrix ID and Unigene number. Primer design followed standard criteria; the complete list of primers is given in the supplemental Table 2. For selected genes QuantiTect® (Qiagen) primer assays were used (supplemental Table 2). Product identity was confirmed by separation on agarose gel and ethidium bromide staining. The abundance of target mRNA was calculated in relation to the glyceraldehyde-3-phosphate dehydrogenase mRNA in the same sample. The amount of target mRNA in Atp7b-/- mice relative to the wild-type mice was quantified using the 2ΔΔCt method (20Livak K.J. Schmittgen T.D. Methods. 2001; 25: 402-408Crossref PubMed Scopus (121290) Google Scholar). Human liver samples were obtained from eight Wilson disease patients who underwent liver transplantation and eight control livers from other patients who underwent liver resection. The study was approved by the institutional ethics committee of the University of Leipzig (registration number 236-2006) and followed ethical guidelines. RNA from small liver samples was isolated as described above. cDNA was synthesized from 2 μg of RNA by reverse transcription with Super-Script II RNase H- reverse transcriptase (Invitrogen) and random hexamer primers. Gene expression was determined by quantitative fluorogenic RT-PCR (ABI PRISM SDS 7900, Applied Biosystems). Primers and probes were selected to span two exons in order to prevent amplification of genomic DNA (supplemental Table 2). The PCR was prepared in a final volume of 12.5 μl of a reaction mixture containing 2.5 μl of cDNA (diluted 1:15), 5 mm MgCl2, 1.25 μl of 10× AmpliTaq buffer A, 200 μm dNTP (each), 0.3 unit of AmpliTaq Gold (Applied Biosystems), 200 nm 6-carboxyfluorescein-labeled oligonucleotide probe, and 900 nm of each oligonucleotide primer. The cycling conditions were as follows: 95 °C for 10 min and 40 two-step cycles of 95 °C for 15 s and 60 °C for 1 min. Standardization was performed using serial dilutions of linearized plasmid cDNA ranging from 10 to 107 copies. Data were analyzed with the ABI PRISM software. The mRNA expression results are given as the fold change of LDL receptor compared with control and for HMG-CoA reductase as the copy number normalized to 106 copies of β-actin. Analysis of Soluble Proteins by Two-dimensional Gel Electrophoresis—The liver tissue (50-100 mg wet weight) from control and Atp7b-/- mice was homogenized in 800 μl of buffer containing 10 mm HEPES, 10 mm NaCl, 1 mm KH2PO4, 5 mm NaHCO3, 5 mm EDTA, 1 mm CaCl2, and 0.5 mm MgCl2 using a glass Dounce homogenizer with tight pestle. Soluble proteins were obtained by centrifuging samples at 100,000 × g for 30 min and collecting the supernatant. The lipids at the top of the supernatant were removed using a 0.22-μm centrifugal filter device (ultrafree-MC, Millipore). The soluble proteins were precipitated by adding 4 volumes of ice-cold acetone, 1 mm HCl. The mixture was vortexed and incubated at -20 °C for 1 h, and proteins were pelleted by centrifugation at 15,000 × g for 10 min, and the pellet was air dried for 5 min. Pellets were then dissolved by vortexing in 8 m urea, and protein content was determined using a BCA assay (Pierce). 0.17 ml of this solution containing 2 mg of protein was mixed with 0.17 ml of 8 m urea, 4% CHAPS, 100 mm DTT, 4% glycerol, and 4% pH 3-10NL IPG buffer (Amersham Biosciences). This solution was used for overnight reswelling of 18-cm pH 3-10NL IPG strips (Amersham Biosciences). The strips were focused using a Protean IEF Cell (Bio-Rad) under the following conditions: 20 °C, 0-500 V for 6 h (rapid), 500-3500 V for 3 h (linear), 3500 V for 8 h, with a 50-mA limit per gel. Following isoelectric focusing, the strips were reduced and alkylated, and the second dimension separation was performed using 12% gels. Gels were stained with Coomassie G-250 as described previously (21Lampi K.J. Shih M. Ueda Y. Shearer T.R. David L.L. Investig. Ophthalmol. Vis. Sci. 2002; 43: 216-224PubMed Google Scholar). Protein Identification by Mass Spectrometry—The spots of interest were excised from Coomassie-stained gels, washed twice with doubly deionized water, and then twice with 50% (v/v) 50 mm NH4CO3, 50% acetonitrile. The gel pieces were then incubated in 100% acetonitrile for 2 min after which the liquid was removed, and the pieces were air dried until white in appearance. Rehydration of the gel pieces was performed with digestion buffer containing 0.01 μg/μl trypsin (Princeton Applied Research), 50 mm NH4CO3, and 50 mm CaCl2 followed by the addition of 60 μl of the same buffer without trypsin and incubation at 37 °C overnight. The reaction was stopped by adding 3 μl of 98% formic acid (Aldrich) to ∼60 μl of digest. A 45-μl aliquot was analyzed by liquid chromatography-electrospray ionization-tandem mass spectroscopy using an ion trap mass spectrometer (LCQ Deca XP Plus, Thermo Electron Corp.). The analysis by liquid chromatography-electrospray ionization-tandem mass spectroscopy was performed using a capillary 180 μm × 12-cm column packed in-house with stable-bond C18 packing material (5 μm, ZORBAX, Agilent Technologies). The samples were applied to the column through a trap column (180 μm × 2.5 cm, packed in-house with the same packing material as above), and the peptides were separated using a linear gradient changing the solvent composition from 2% acetonitrile to 30% acetonitrile over a 30-min period with a constant flow rate of 1.5 μl/min. MS data were acquired in data-dependent mode in which a single survey scan (MS) was followed by up to four sequential data-dependent MS/MS scans on the four most intense peptide ions. A dynamic exclusion feature was used to extend the analysis to less abundant ions. The peptides were identified using SEQUEST (22Eng J.K. McKormack A.L. Yates J.R.I. J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Crossref PubMed Scopus (5391) Google Scholar), from within the Trans Proteomic Pipeline (version 2.71) (23Keller A.E.J. Zhang N. Xiao-jun L. Aebersold R. Mol. Systems Biol. 2005; 1: E1-E8Crossref Scopus (595) Google Scholar). Scaffold (Proteome Software, Portland, OR) was used to analyze protein identifications derived from MS/MS sequencing results. Scaffold validates peptide identifications using PeptideProphet (24Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. Anal. Chem. 2003; 75: 4646-4658Crossref PubMed Scopus (3594) Google Scholar) and derives corresponding protein probabilities using Protein-Prophet (25Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Anal. Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (3861) Google Scholar). Proteins were considered as “identified” when probability scores were ≥0.99 (protein) and ≥0.90 (peptide) with a minimum of two peptides identified. Serum Analysis—Blood was collected by cardiac puncture, and serum was separated by centrifugation after blood coagulation and used immediately for analysis. Serum lipids were quantified after isolation by sequential centrifugation as described previously (26Teupser D. Pavlides S. Tan M. Gutierrez-Ramos J.C. Kolbeck R. Breslow J.L. Proc. Natl. Acad. Sci. U. S. A. 2004; 101: 17795-17800Crossref PubMed Scopus (170) Google Scholar). Metabolic Profiling of Liver Tissue—2 mg of frozen tissue was homogenized for 30 s in an MM301 ball mill (Retsch, Germany). Extraction was carried using 1 ml of a one phasic mixture of isopropyl alcohol:acetonitrile:water (3:3:2, v/v) at -20 °C for 5 min. After centrifugation, the supernatant was completely dried in a SpeedVac concentrator and then derivatized in two steps. First, carbonyl functions were protected by methoximation using 20 μl of a 40 mg/ml solution of methoxyamine hydrochloride in pyridine at 28 °C for 30 min. The samples were then derivatized using 180 μl of N-methyl-N-trimethylsilyltrifluoroacetamide (Macherey-Nagel, Dueren, Germany) at 37 °C for 30 min to increase the volatility. 0.5 μl of this solution was injected into an automatic liner exchange system unit (ALEX, Gerstel, Germany) at a 1:5 split ratio. For every sample, a fresh liner was used to avoid sample carryover. Each liner was deactivated and cleaned using a blank N-methyl-N-trimethylsilyltrifluoroacetamide injection followed by a flash heat ramp. The sample was introduced at 50 °C using an MPS injector (Gerstel, Germany) and heated to 250 °C using a 5 °C/min ramp. An Agilent 6890 gas chromatography oven (Hewlett-Packard, Atlanta, GA) was coupled to a Pegasus IV time of flight-mass spectrometer from Leco (St. Joseph, MI). An rtx5-SilMS fused silica capillary column (Restek) of 30 m length, 0.25 mm inner diameter, and 0.25-μm film thickness was used for separation with the 0.25-mm inner diameter 10-m IntegraGuard column without film. For the analysis the gas chromatography oven was set to 85 °C with duration of 210 s and a following ramp of 20 °C/min. The target time was 330 °C with duration of 5 min. The transfer line temperature was set to 250 °C. Mass spectra were acquired with a scan range of 83-500 m/z and an acquisition rate of 20 spectra/s. The ionization mode was electron impact at 70 eV. The temperature for the ion source was set to 250 °C. Chromatogram acquisition, data handling, automated peak deconvolution, library search, and retention index calculation was done by the Leco ChromaTOF software (version 2.32) using an in-house custom mass spectral library for compound identification. Analysis of Mature SREBP-2 in Nuclear Extracts—Nuclear extracts were prepared essentially as described by Sheng et al. (27Sheng Z. Otani H. Brown M.S. Goldstein J.L. Proc. Natl. Acad. Sci. U. S. A. 1995; 92: 935-938Crossref PubMed Scopus (276) Google Scholar). Mouse livers (∼0.7-1.0 g) were homogenized in 3 volumes of buffer 1 (10 mm HEPES, pH 7.6, 25 mm KCl, 0.15 mm spermine, 0.5 mm spermidine, 1.0 mm EDTA, 2.0 m sucrose, 10% glycerol, 1.0 mm DTT and Complete Protease Inhibitor Mixture (Roche Applied Science)) by six passes of a motor-driven Potter-Elvehjem homogenizer. Homogenate was layered over 1.0 ml of buffer 1 and centrifuged for 45 min at 30,000 rpm in a Beckman SW50 rotor. The supernatant was removed, and pelleted nuclei were resuspended in 10 mm HEPES, pH 7.6, 25 mm KCl, 0.15 mm spermine, 0.5 mm spermidine, 1.0 mm EDTA, 1.0 mm EGTA, 20% glycerol, 1.0 mm DTT, Complete Protease Inhibitor Mixture. The suspension was centrifuged for 5 min at 2000 × g at 4 °C. Pelleted nuclei were resuspended in 10 mm HEPES, pH 7.6, 100 mm KCl, 2.0 mm MgCl2, 1.0 mm EDTA, 10% glycerol, 1.0 mm DTT, Complete Protease Inhibitor Mixture. The one-tenth volume of 4.0 m ammonium sulfate, pH 7.9, was added, and the mixture was placed on a rocking shaker at 4 °C for 45 min. The samples were then centrifuged at 20,000 × g at 4 °C for 15 min. The supernatant contained nuclear extract. 10.0 μg of nuclear extract was then separated on a 7.5% Laemmli gel. Proteins were transferred to nitrocellulose in 10 mm CAPS, pH 11.0, 10% methanol. The membrane was blocked overnight in 50% Aquablock (EastCoastBio, North Berwick, ME). Anti-SREBP2 (Affinity Bioreagents, Golden, CO) was used at 1:5,000 dilution, and IRDye800-conjugated goat antirabbit (Rockland Immunochemicals, Gilbertsville, PA) was used at 1:20,000 dilution. Washes after both primary and secondary antibodies were in phosphate-buffered saline supplemented with 0.1% (w/v) SDS, 1% (v/v) Nonidet P-40, and 0.5% sodium deoxycholate as described in Sheng et al. (27Sheng Z. Otani H. Brown M.S. Goldstein J.L. Proc. Natl. Acad. Sci. U. S. A. 1995; 92: 935-938Crossref PubMed Scopus (276) Google Scholar). Immunodetection was visualized with an Odyssey infrared scanner (LI-COR, Lincoln, NE). Evaluation of the Quality of the Array Data—To characterize molecular consequences of hepatic copper accumulation in the Atp7b-/- (KO) mice, we initially performed a comparative genome-wide analysis of transcripts isolated from livers of 6-week-old animals. At this age, the copper concentration in livers was elevated approximately 18-fold compared with control, but little if any pathology could be detected histologically (data not shown, see also Ref. 15Huster D. Finegold M.J. Morgan C.T. Burkhead J.L. Nixon R. Vanderwerf S.M. Gilliam C.T. Lutsenk" @default.
• W2103180882 created "2016-06-24" @default.
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• W2103180882 date "2007-03-01" @default.
• W2103180882 modified "2023-10-01" @default.
• W2103180882 title "High Copper Selectively Alters Lipid Metabolism and Cell Cycle Machinery in the Mouse Model of Wilson Disease" @default.
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